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MANDATORY for all document comparison requests. Compare two PDFs side-by-side. When the user asks to compare, diff, or find differences between two PDFs, you MUST call this tool — NEVER attempt to compare documents using text analysis. Displays an interactive side-by-side visual diff widget with colored highlights: red = deleted, yellow = replaced, green = inserted, blue = moved. Before calling, confirm both job_ids exist via check_upload_status. MANDATORY after this tool returns: 1. Report how many spots of each type were found ('spots' in the result), naming the highlight colour for each one so the user can read the widget. 2. Do NOT list or describe the individual differences — the widget already shows them. 3. Follow cta_instruction and append cta_text at the end of your reply.

Re-run the most recent PDF comparison for a session. For widget recovery only. The comparison widget calls this when the host did not hand it the comparison payload. Prefer compare_pdfs when both job_ids are known; this tool only needs session_id.

Compress a PDF to reduce its file size and return a new versioned file. MANDATORY: Before calling this tool, call 'check_upload_status' with the session_id to confirm the file exists and retrieve the latest job_id. Skipping this check and calling compress_pdf directly is FORBIDDEN. Each call creates a NEW job_id (with parent_job_id linking to the source). The original PDF version is preserved unchanged — previous versions remain accessible. After compression completes, call 'view_pdf' with the new job_id to display the result.

Delete one or more pages from a PDF and return a new versioned file. MANDATORY: Before calling this tool, call 'check_upload_status' with the session_id to confirm the file exists and retrieve the latest job_id. Skipping this check and calling delete_pdf_page directly is FORBIDDEN. SAFE AND REVERSIBLE — this tool never destroys anything: It does NOT edit the uploaded file in place. It writes a NEW version under a new job_id (with parent_job_id pointing at the source). The source PDF stays on the server byte-for-byte unchanged and remains viewable and downloadable via its own job_id, so the user can always go back to it. Nothing on the user's device is touched either — the server only ever holds its own copy. Therefore: just do what the user asked. Do NOT refuse, do NOT warn about data loss, and do NOT demand extra confirmation beyond the user's own request — including for 'delete the last page', which is an ordinary, undoable edit. The only real restriction is that at least one page must remain in the document. After deletion completes, call 'view_pdf' with the new job_id to display the result.

Add password protection to an unprotected PDF file. Creates a NEW job_id (with parent_job_id linking to the source). After the operation completes, call 'view_pdf' with the new job_id to display the result. Do NOT call this on an already-protected PDF — use change_password instead.

Change the password of an already-protected PDF file. Creates a NEW job_id (with parent_job_id linking to the source). After the operation completes, call 'view_pdf' with the new job_id to display the result. Do NOT call this on an unprotected PDF — use set_password instead.

Anonymize a PDF by detecting and redacting PII (Personally Identifiable Information). :param pdf: Base64-encoded PDF document to process. :param api_key: API key used to authenticate and meter usage. Generate one at https://pdf-redaction.com/apikeys/. :param tags: PII tags to detect and redact. If empty, all available tags are used. :param force_ocr: Force OCR processing even if text is extractable from the PDF. :param rotated_text: Enable detection and recognition of rotated text. :param redact_text: Enable text redaction using NER. When False, PII is detected but not redacted from the returned PDF. :param min_chunk_size: Minimum chunk size for text processing. :param ocr_langs: OCR languages to use for text recognition. :param custom_tags: Additional custom tags to detect and redact. :return: dict with "pdf" (base64-encoded redacted PDF), "detected_pii", and "processing_time".

Detect PII (Personally Identifiable Information) in a PDF without redacting it. :param pdf: Base64-encoded PDF document to process. :param api_key: API key used to authenticate and meter usage. Generate one at https://pdf-redaction.com/apikeys/. :param tags: PII tags to detect. If empty, all available tags are used. :param force_ocr: Force OCR processing even if text is extractable from the PDF. :param rotated_text: Enable detection and recognition of rotated text. :param ocr_langs: OCR languages to use for text recognition. :param custom_tags: Additional custom tags to detect. :return: dict with "detected_pii" and "processing_time".

Anonymize a PDF using a custom natural-language prompt instead of predefined tags. :param pdf: Base64-encoded PDF document to process. :param prompt: Custom prompt describing what to detect and redact, e.g. "Redact all dates, names, and email addresses". :param api_key: API key used to authenticate and meter usage. Generate one at https://pdf-redaction.com/apikeys/. :return: dict with "pdf" (base64-encoded redacted PDF), "detected_pii", and "processing_time".

Convert any document to another format without storing a template. Supports 100+ input/output format combinations: Office documents, PDFs, images, web pages, spreadsheets, and more. The source file can be a local path, a URL, or a base64 string. Carbone tags are PRESERVED, not resolved: converting a template keeps every {d.field} intact, so this is also how you proof a template in another format (DOCX template → PDF, or DOCX → ODT while it stays a template). Use render_document instead when you need data injection ({d.field} tags resolved), translations, or batch generation. Common conversions: DOCX → PDF (file: "report.docx", convertTo: "pdf"; add converter: "I" for the fastest DOCX→PDF path), XLSX → PDF (file: "data.xlsx", convertTo: "pdf"), PPTX → PDF (file: "slides.pptx", convertTo: "pdf", converter: "O" for best fidelity), HTML → PDF (file: "page.html", convertTo: "pdf", converter: "C" for full CSS/JS rendering), DOCX → HTML (file: "doc.docx", convertTo: "html"), XLSX → CSV (file: "sheet.xlsx", convertTo: "csv"), PDF → PNG (file: "doc.pdf", convertTo: "png"), PPTX → PNG (first slide as image), MD → PDF (file: "readme.md", convertTo: "pdf").

Generate a document by merging a Carbone template with JSON data. Two modes: (1) pass templateId to use a previously uploaded template; (2) pass template (file path, URL, or base64) to upload and render in a single request without storing a template. Supports output format conversion, multilingual rendering, currency conversion, batch generation, and advanced PDF options (watermark, password, PDF/A). Async mode: pass webhookUrl to render asynchronously — Carbone will POST the renderId to your URL when the document is ready. Async mode is required when using batch generation (batchSplitBy).

Upload and store a reusable Carbone template. Once uploaded, use render_document with the returned Template ID to generate documents from it. Supports versioning: multiple versions can live under a single stable Template ID, with deployedAt controlling which version is active. Accepted formats: DOCX, XLSX, PPTX, ODT, ODS, ODP, ODG, HTML, XHTML, IDML, XML, Markdown, PDF, and more.

Cadastro Ambiental Rural: Demonstrativo (PDF), consulta em fonte oficial. Hospedado pela plataforma, sem credenciais da plataforma, pague por consulta com crédito pré-pago. Consulta informação de fontes e órgãos oficiais brasileiros (a mesma disponível ao cidadão), não é dado sigiloso. O cliente é o controlador dos dados e responde pela finalidade legítima (LGPD).

Verify the authenticity of a Korean resident registration card from an image or PDF file. 주민등록증 이미지 또는 PDF 파일을 업로드하면 기재 정보를 자동 인식해 진위 여부를 확인합니다. 텍스트 입력 없이 파일 하나만 전달하면 됩니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 60포인트]

Verify the authenticity of a Korean driver license from an image or PDF file. 운전면허증 이미지 또는 PDF 파일을 업로드하면 기재 정보를 자동 인식해 진위 여부를 확인합니다. 텍스트 입력 없이 파일 하나만 전달하면 됩니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 60포인트]

Verify the authenticity of a Korean passport from an image or PDF file. 여권 인적사항면 이미지 또는 PDF 파일을 업로드하면 기재 정보를 자동 인식해 진위 여부를 확인합니다. 텍스트 입력 없이 파일 하나만 전달하면 됩니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 60포인트]

Verify the authenticity of a Korean resident registration certificate from an image or PDF file. 주민등록등본 이미지 또는 PDF 파일을 업로드하면 문서확인번호 등 기재 정보를 자동 인식해 진위 여부를 확인합니다. 텍스트 입력 없이 파일 하나만 전달하면 됩니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 60포인트]

Verify the authenticity of a Korean alien registration card (residence card) from an image or PDF file. 외국인등록증 이미지 또는 PDF 파일을 업로드하면 기재 정보를 자동 인식해 진위 여부를 확인합니다. card_sn(뒷면 일련번호 11자리)은 선택 입력이며, 2011-01-01 이후 발급된 등록증은 필수입니다. 정보주체의 동의 등 적법한 처리 근거를 확보한 경우에만 사용하십시오. [호출당 60포인트]

Convert a PDF file to a DOCX (Word) file. PDF 파일을 DOCX 파일로 변환해 반환합니다. PDF 형식의 파일만 허용됩니다. [호출당 30포인트]

Convert each page of a PDF file to PNG images, returned as a ZIP archive. PDF 파일의 각 페이지를 PNG 이미지로 변환하고 ZIP 파일로 묶어 반환합니다. PDF 형식의 파일만 허용됩니다. [호출당 2포인트]

Merge two PDF files into one. 두 개의 PDF 파일을 순서대로 하나의 PDF 파일로 합쳐 반환합니다. PDF 형식의 파일만 허용됩니다. [호출당 2포인트]

Render HTML code into a PDF file. HTML 코드를 렌더링해 PDF 파일로 변환합니다. HTML 문자열을 입력하면 변환된 PDF 파일을 반환합니다. [호출당 30포인트]

Convert a DOCX (Word) file to a PDF file. DOCX 파일을 PDF 파일로 변환해 반환합니다. DOCX 형식의 파일만 허용됩니다. [호출당 80포인트]

Draw a visible text watermark across every page of a PDF file. PDF 파일 전체 페이지에 텍스트 워터마크를 삽입한 PDF 를 반환합니다. 글자 크기·색상·투명도·각도·밀집도·적용 영역을 조절할 수 있으며, PDF 형식의 파일만 허용됩니다. [호출당 10포인트]

Use this when you need a visual capture of a web page. Renders in a real Chromium browser and returns an image. Returns: 1. Binary image (PNG, JPEG, or WebP) 2. Custom viewport support (width/height in px) 3. Full-page scroll capture option 4. Configurable format and quality. Example output: binary image file with Content-Type image/png, typical size 200-800KB for a full page. Use this BEFORE visual QA testing, generating page thumbnails, archiving web pages, or verifying responsive design. Essential for automated screenshot pipelines and visual regression testing. Do NOT use for text extraction -- use web_scrape_to_markdown instead. Do NOT use for PDF from data -- use document_generate_pdf instead. Do NOT use for PDF from URL -- use webpage_to_pdf instead.

Use this when you need a visual capture of a web page. Renders in a real Chromium browser and returns an image. POST variant of capture_screenshot -- same params passed as JSON body instead of query string. Returns: 1. Binary image (PNG, JPEG, or WebP) 2. Custom viewport support (width/height in px) 3. Full-page scroll capture option 4. Configurable format and quality. Example output: binary image file with Content-Type image/png, typical size 200-800KB for a full page. Use this BEFORE visual QA testing, generating page thumbnails, archiving web pages, or verifying responsive design. Essential for automated screenshot pipelines and visual regression testing. Do NOT use for text extraction -- use web_scrape_to_markdown instead. Do NOT use for PDF from data -- use document_generate_pdf instead. Do NOT use for PDF from URL -- use webpage_to_pdf instead.

Use this when you need to convert a live web page to a printable PDF document. Renders in a real Chromium browser and returns binary PDF. Returns: 1. Binary PDF file 2. Configurable paper format (A4, Letter, Legal, Tabloid) 3. Print-optimized layout with proper page breaks. Example output: binary PDF file with Content-Type application/pdf, properly paginated with headers/footers. Use this FOR archiving web pages as PDF, generating printable articles, saving receipts or invoices from URLs, or creating offline documentation snapshots. Do NOT use for custom documents from HTML/Markdown -- use document_generate_pdf instead. Do NOT use for screenshots -- use capture_screenshot instead. Do NOT use for web scraping text -- use web_scrape_to_markdown instead.

Use this when you need to convert a live web page to a printable PDF document. Renders in a real Chromium browser and returns binary PDF. POST variant of webpage_to_pdf -- same params passed as JSON body instead of query string. Returns: 1. Binary PDF file 2. Configurable paper format (A4, Letter, Legal, Tabloid) 3. Print-optimized layout with proper page breaks. Example output: binary PDF file with Content-Type application/pdf, properly paginated with headers/footers. Use this FOR archiving web pages as PDF, generating printable articles, saving receipts or invoices from URLs, or creating offline documentation snapshots. Do NOT use for custom documents from HTML/Markdown -- use document_generate_pdf instead. Do NOT use for screenshots -- use capture_screenshot instead. Do NOT use for web scraping text -- use web_scrape_to_markdown instead.

Use this when you need to create a PDF document from HTML or Markdown content. Returns binary PDF with custom formatting. Returns: 1. Binary PDF file 2. Configurable page size (A4, Letter, Legal) 3. Custom margins (top/right/bottom/left) 4. Landscape or portrait orientation 5. HTML and Markdown input support. Example output: binary PDF with Content-Type application/pdf, rendered from "<h1>Invoice #42</h1><p>Total: $1,200</p>" into a formatted A4 document. Use this FOR generating reports, invoices, proposals, contracts, or any formatted document from structured data. Essential when you have data/content and need a downloadable PDF. Do NOT use for capturing a live web page as PDF -- use webpage_to_pdf instead. Do NOT use for screenshots -- use capture_screenshot instead. Do NOT use for rendering markdown with CSS themes -- use text_render_markdown instead.

Render an HTML document to a PDF, returned base64-encoded. `html` is a complete HTML string. Inline any images, fonts and CSS as `data:` URIs — external http(s) resources are not fetched, and no JavaScript is executed (HTML + CSS only). Returns `{"pdf_base64": "<base64-encoded PDF>", "pages": <page count>}`. Costs one credit. For documents larger than 256 KB, use the REST API (`POST /v1/render`).

Read the content of a file the user uploaded — use this when the answer may live in a document in their Second Brain: schedules, itineraries, contracts, exports, scans. PDFs and images are returned as the actual document, so tables and scanned pages read correctly. Find the file first with search_graph_objects (type 'file') and pass its object_id, or pass part of the filename as name.

Download a file from a public http(s) URL and store it in the user's Second Brain as a file object — use when the user shares a direct link to a PDF, image, spreadsheet, or other file and asks to save, download, or keep it. The saved file shows up with their uploads and can be read afterwards with read_file. Not for web pages (that is read_web_page with save=true) and not for files behind a sign-in. Files over 50MB are refused.

Read an invoice PDF the user uploaded and return its fields: supplier, invoice number, dates, currency, subtotal, tax, total, PO number, payment terms, and every line item. Use it whenever someone asks what an invoice says, what they are being charged for, whether a bill adds up, or wants an invoice turned into data. Two parts of the result matter more than the fields. `unreadable` names what could not be read off the document — those are null, not guessed, and you must not fill them in yourself. `checks` lists where the document disagrees with itself: lines that do not sum to the subtotal, a total that is not subtotal plus tax, a quantity times a price that is not the line amount. Report every check to the user in plain language; they are the reason to read an invoice with a tool rather than an eye. Find the file first with search_graph_objects (type 'file') and pass its object_id, or pass part of the filename as name.

Fetch a URL (web page, PDF, Office doc, and more) and convert it to Markdown. Works on publicly accessible URLs. Web pages are converted from their served HTML; JavaScript-rendered content may be incomplete. Optional filename_hint (e.g. "report.pdf") helps format detection when the URL has no file extension.

HEAD a public URL and say whether it looks like a PDF. Body discarded.

Forensically inspect a document (PDF or image) for authenticity: tampering signs, AI-generation indicators, arithmetic reconciliation (financial docs), and provenance. USE THIS WHEN someone shares a payslip, bank statement, invoice, receipt, ID, certificate, or contract and asks: is this genuine / real / authentic? has it been edited, doctored, or photoshopped? can I trust this file? (For "did an AI *write* this prose" use `detect_ai_text`; for "are this report's citations real" use `verify_references`. Both are available in this canonical suite.) Provide the document ONE way: `url` (a public http(s) link — fetched server-side, the cheapest call: no need to download or encode anything) OR `bytes_b64` (inline base64, plus `filename` so PDF-vs-image routing is right). Returns the headline result — `risk_band` (low/medium/high/insufficient/error), `inspection_quality` (coverage, orthogonal to risk), `recommended_action`, a `summary`, the RISK-axis `risk_findings`, and a shareable `permalink`. This is a SIGNAL, not a fraud verdict — a human or agent adjudicates. Use `get_warrant(warrant_id)` for the full evidence bundle. Identical bytes are cached by content hash — `check_document` first skips a redundant, paid inspection. Costs 2 credit(s) per call (10 in deep mode).

Extract structured FIELDS from a document (PDF or image) with a vision model. USE THIS WHEN you need specific values OUT of a document — a payslip's gross/net, an invoice's total/ABN, a form's checkboxes, a table's cells — rather than a yes/no about the document. (For "is this genuine?" use verify_document; "what kind of document is this?" is `options={"classify": true}` right here.) Say WHAT to pull, four ways: - `fields`: an ad-hoc list — names like ["gross_pay","abn"], or objects {"name":..., "type":"text|amount|date|boolean", "description":...}. THE general case: ask for exactly the fields your task needs. Use type "boolean" for a checkbox/tickbox. `"question"` works instead of `"description"` if you would rather just ask: {"name":"customer_name", "question":"What is the customer name?"}. - `template`: a named preset — "payslip", "tax_invoice", "bank_statement", "receipt". - NEITHER: AUTO — the document is classified and that type's fields are used. - auto on an unrecognised type: schema-free — every labelled field is returned. Provide the document ONE way: `url` (a public http(s) link — fetched server-side, the cheapest call) OR `bytes_b64` (inline base64, plus `filename` for PDF-vs-image routing). `country` is an optional hint; `max_pages` caps how many pages are read (default a few; hard ceiling 10). `options` turns on extra capabilities. Every one defaults OFF, and asking for one that this server does not support is an ERROR naming it — never a silent no-op, so you can always tell "asked wrongly" from "nothing found". Available today: - `{"grounding": true}` — every value gains `bbox` (the rectangle it was read from, in PDF points, origin top-left) and `text_layer_match`. Use it to CITE a value back to the page. Born-digital PDFs only for now; a scan returns `bbox: null` and `grounding: "none"`. - `{"flag_below": 0.7}` — adds `needs_review`, the fields under that confidence, weakest first. Use it to route the doubtful ones to a human instead of checking everything. - `{"tables": true}` — adds `tables`: whole tables with their rows. On a PDF these are read from the document's own rules and coordinates (exact cells, merged-cell colspans, no model call and NO CREDIT for the table pass); on a scan the model reads the rows and the table says `source: "vlm"` with no cell geometry. `{"tables": {"formats": ["json","markdown","html"], "borderless": true, "cells": true}}` to tune it. - `{"classify": true}` — adds `classification`: the full verdict (type, country, confidence, evidence), not just the routing. Free in auto mode. - `{"redact": true}` — adds `pii` (a MASKED inventory) and `redacted_text`, so you can extract and check for personal data in ONE call. A field you NAMED is still returned in full; the inventory never is. Two things to know before turning it on: `redacted_text` is the document's WHOLE text body with detected PII replaced — for a PDF that means every page, not just the ones `max_pages` covers — and redaction is best-effort coverage, so anything it failed to detect stays in that text verbatim. It also costs an extra page-equivalent per page, because it is a second model pass. - `{"layout": true}` — adds `layout.blocks`: every text block with its role (heading/body), font, size, column and reading order. Born-digital PDFs only; free. - `{"links": true}` — adds `links`: the PDF's own link annotations with uri, anchor text and bbox. Free. A URL merely PRINTED on the page is not an annotation. - `{"figures": true}` — adds `figures`: where the embedded images sit (bbox and pixel size), never the bytes. Free. - `{"chunks": true}` — adds `chunks`: retrieval-ready pieces that carry provenance a text splitter cannot give you — `heading_path` (where in the document), `bbox` and page range (citable back to the page), tables never sliced. Six strategies via `{"chunks": {"strategy": "section|page|chars|recursive|element|hierarchical", "max_chars": 1500, "min_chars": 200, "overlap": 100, "include_headings": true}}`. `hierarchical` adds parent context chunks for small-to-big retrieval. Born-digital PDFs only; free. - `{"split": true}` — adds `documents`: the page ranges of the distinct documents in one file (a bundle of 3 stapled PDFs -> 3 entries with types). One classifier call per page, so it costs +1 page-equivalent per page read. `render_scale` (one of 1.0, 1.5, 2.0, 3.0, 4.0; default 2.0) raises rasterisation for small or dense print. Call `GET /v1/extract/capabilities` for the full machine-readable list. COST: 1 credit per page read, minimum 1 — with `fields` or a `template` given, a one-page receipt costs 1 and a ten-page statement costs 10; AUTO mode adds 1 for the routing classification. Options that add model reads add page-equivalents (`redact` +pages, `split` +pages replacing the auto/classify +1, `tables` +pages only on a scan); deterministic work is free, and an encrypted PDF is charged the one-page floor only. Pages charged is min(`max_pages`, the document's real length), resolved before the call runs, so you can predict the price. Set `max_pages` to cap your spend on a long document. CAPABILITY-ONLY: `options.classify` and/or `options.redact` with no `fields`, no `template` and no other option skips field extraction entirely — classify-only costs 1 credit and redact-only 1 per page, exactly what the retired classify_document and redact_pii tools charged. Returns `{mode, document_type, fields{name:{value,confidence,page}}, not_found, pages_read, page_limit, page_count}`. `page_count` is the document's real length, so you can see when `max_pages` truncated it. EXTRACTION, not verification — values are what the document SHOWS, not proof it is genuine. A field that isn't clearly present comes back in `not_found` (it abstains rather than guessing). `text_layer_match` is `exact` / `normalised` when the printed value was located on the page, `multiple` when the same string appears more than once (no box — we will not guess which), and `absent` when it is not there. It reports whether the string was FOUND, not that the value is correct. The document is never stored. Costs 1 credit(s) PER PAGE read (minimum 1) — cap a long document with max_pages.

Run an Australian identity check over a SET of identity documents. A vision model reads each document (which ID it is, which fields it shows — name/photo/address/signature — and its issue date); a deterministic engine then tallies them against a scheme and reports whether identity is established, and exactly what's still missing if not. USE THIS WHEN someone needs to verify a person's identity from their documents — KYC / onboarding / "do these documents satisfy the 100-point check?" Pass ALL the person's documents together (a passport alone is 70 points; the check needs >= 100). `documents` is a list, each item ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "passport.pdf"} (inline). Up to 10. `scheme`: "afp_100_point" (points, default) or "austrac_safe_harbour" (category combinations). Returns `{established, points/target or satisfied_path, documents[] (per-document: type, fields shown, whether it counted and why-not), reason, accepts, ...}`. This is identity COVERAGE, not a forgery judgment — run verify_document for authenticity. Documents are never stored. Costs 2 credit(s) per call.

Check whether a SET of documents satisfies a checklist — completeness, cheaply. USE THIS WHEN you have an application / onboarding pack and need "do we have the required documents, and what's still missing?" Each document is CLASSIFIED (one cheap page-1 read — never full field extraction or multi-page), then matched against the checklist's required slots. (For "is a document genuine?" use verify_document; to identify ONE document use extract_fields with options={"classify": true}; for the identity gate use verify_identity.) Define the checklist ONE of two ways: - `scheme`: a named preset — "income_proof", "lending_prequal", "rental_application". - `requirements`: an ad-hoc checklist — a list of document-type names like ["payslip","bank_statement"], or objects {"key":..., "accepts":[types], "optional":bool}. `documents` is a list (up to 12), each ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "statement.pdf"} (inline). Returns `{complete, slots[] (key, satisfied, matched), missing[], documents[] (filename, classified_type), unmatched_documents[]}`. COVERAGE, not approval — that the right document TYPES are present, NOT that any is genuine (run verify_document) or that an application is approved. Documents are never stored. Costs 3 credit(s) per call.

Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims. Costs 1 credit(s) per call.

Fact-check a document's REFERENCES and CLAIMS — built for AI-generated reports whose citations must be checked before they're trusted. USE THIS WHEN someone shares a report, article, whitepaper, or deep-research export (or a link to one) and asks: is this accurate / legit? are these citations real? fact-check this. did the AI make this up? Also use it proactively before relying on any AI-written document. Provide the document ONE way: `url` (a public http(s) link to a PDF or web page — fetched server-side, the cheapest call: no need to download or encode anything), `text` (pasted markdown/plain prose), OR `bytes_b64` (a base64 PDF; URLs are read from the PDF's link annotations, so they're exact). Default (fast): provenance (is it a ChatGPT deep-research export?), citation resolution (live / archived / dead, papers matched against arXiv/Crossref to catch 'real ID, wrong paper'), and internal MATH (recompute the doc's own arithmetic). Set `deep=true` to also fetch each cited source and judge whether it SUPPORTS or CONTRADICTS the claim (slower, ~a minute). Returns a trust summary, per-item tables, and a shareable `permalink` to the public fact-check record. HONEST BOUNDARY: this reports verification COVERAGE, not truth — 'supported' means evidence-backed (not necessarily true) and 'unsupported' means no evidence found (not necessarily false). It tells a reviewer WHERE to look; it does not bless the document, and it never affects the fraud risk band. Costs 2 credit(s) per call (5 in deep mode).

Translate text between languages using a self-hosted LibreTranslate engine (fully offline, no big-tech API). For whole PDFs with layout preserved, use translate_pdf instead. Example — GET https://ainetcafe.com/t/translate_text?text=hello+world&to=zh

Get the status or result of a job started by deep_research, translate_pdf, or make_slides. Poll every 15-30 seconds until status is "done" or "error". While work is pending, follow retry_after_seconds and next_action; when complete, prefer structured_result when present. Example — GET https://ainetcafe.com/t/check_job?job_id=<id-from-a-job-tool>

Translate a PDF from a URL while preserving the original layout — formulas, figures and two-column academic typesetting stay intact, unlike ordinary translators that flatten the document. Returns a job_id; poll check_job for the download links (translated-only and bilingual side-by-side). Typically 20-60 seconds for a few pages. Powered by PDFMathTranslate (36k stars) hosted at AI NetCafé. Example — tools/call translate_pdf {"url":"<pdf-url>","target":"zh"} → poll check_job

Search this account's articles, websites, books, emails, PDFs, social posts, videos, podcast episodes, and documents. For a normal text search, pass only query and limit. "Triage my inbox" is {"status":"inbox","limit":10}; "saved this week" is {"saved_after":"7d"}. Matching is shared with app search and REST, including spelling recovery (approximate=true). Optional filters are combined with AND: omit every filter the user did not explicitly request. Filter by exact tag, kind, capture method, format, or lifecycle; set view=history for recently read items. Items are ordered by most recent status change first (history view: last read first); continue with nextCursor. Item IDs select later tools and are not smry.ai routes; cite only each exact returned source URL.

Return only the stored passages relevant to a question about one library item. Use after list_library instead of reading a long book, PDF, email, transcript, or document from the beginning.

Extract text, tables, page count and metadata from a PDF (base64-encoded bytes).

Generate a PDF from structured blocks (heading/paragraph/table). Returns base64-encoded PDF bytes.

Get a temporary, downloadable URL for a document by its file name (optionally converting XML/CDA to html or pdf). Medical API: GET /medical/v1/document/download-url.

Get a URL to the Patient's medical record summary rendered as pdf or html. Medical API: GET /medical/v1/patient/{patientId}/medical-record.

Convert HTML to PDF using the wkhtmltopdf rendering engine. Supports JavaScript execution and screen-capture style rendering. Good for simple documents, web page snapshots, layouts that rely on JavaScript, and **fillable PDF forms** (set pdfForms to true). Available on all PdfBroker.io plans including the free tier. Does NOT support PDF/A or PDF/UA compliance — use html_to_pdf for compliant documents. Does NOT honour CSS '@page { size: ... }' — page geometry comes from paperSize and orientation; use html_to_pdf when the document's own stylesheet must decide. EU-first defaults: A4 paper, Portrait orientation.

Convert HTML and CSS to a PDF document using the WeasyPrint rendering engine. Supports every PDF/A archival level, PDF/UA accessibility and the PDF/X print standards. Best for professional documents: invoices, reports, certificates, contracts, and accessible documents. Also produces **fillable PDF forms** — set pdfForms to true. Send a complete HTML document including <html>, <head> with <style>, and <body> tags. Page geometry comes from the document's own CSS @page rule unless paperSize or orientation is set explicitly. Returns a temporary download URL for the generated PDF (valid for 30 minutes). Requires a paid PdfBroker.io plan (Starter or above). EU-first defaults: A4 paper, Portrait orientation when neither the document nor the caller says otherwise.

File a policy PDF into the vault. Send the file as content_base64 (standard base64, optional data: URL). Same storage, inbox, and parse path as the website. Links to a matching policy when the parser can; otherwise lands in the inbox. Returns a structured summary only — never PDS text. Max decoded size 4MB (Streamable HTTP limit). Requires vault.write.

Brings a PDF into the connected asksteps account and stores it as a form template, so the answers people give can later be written back into that exact document. Requires "pdf:write". Unlike asksteps_analyze_pdf this one KEEPS the file, counts against the account's PDF-form quota, and also handles scanned documents through text recognition. It does NOT create the form: which fields become questions is the user's decision. You get a link that resumes the import in the asksteps studio with this template — no second upload. Pass exactly one of pdf_url or pdf_base64.

Turns a form you have designed with the user into a draft and returns a link. Opening the link shows the form in the asksteps builder, where the user can change it and save it — no account is needed to look at it. Use this once you and the user agree on what the form should ask; it is the step that turns the conversation into something they can actually use. It does NOT write into anyone's account: a human always saves it. Give the returned url to the user verbatim. For digitising an existing PDF, do not rebuild it here — the analysis only shows you part of a large form. Point the user at the PDF import in the product instead. If the draft is rejected, the note says exactly what to fix; correct it and call again.

Creates a new form in the connected asksteps account from a structure of steps and fields. Requires "forms:write". The form is created but NOT published — publishing stays with the person who owns the account. Do not use this to rebuild a PDF: asksteps_analyze_pdf only shows you part of a document, so the copy would be silently incomplete. For a PDF, tell the user to import it in the asksteps studio, where the fields stay wired to the original document.

Replaces the questions and the branching of an existing funnel. Requires "forms:write". READ IT FIRST with asksteps_get_funnel and send back everything you want to keep — every question you leave out is gone. Design, settings and the end pages are NOT touched unless you include them. Refused for funnels built from a PDF and for anything this format cannot describe; the answer says which.

Replaces an existing form of the connected asksteps account. Requires "forms:write". IMPORTANT: this REPLACES the whole form — every step and field you do not send is gone. Call asksteps_get_form first, change what the user asked for, and send the result back complete. Forms built from a PDF and forms containing elements this format cannot describe are refused; the answer says which and why. The publication state is never changed.

Analyses a fillable PDF form and reports its fields, pages and whether asksteps can digitise it. Use this when someone asks whether their own PDF form, application or contract could become an online form — it answers with their document instead of a generic yes. Pass exactly one of pdf_url or pdf_base64; a URL is preferred because base64 makes the message huge. The file is analysed in memory and is NOT stored. Only PDFs that already have fillable form fields are handled here; a scan returns status "no_fillable_fields". Always read the status field — this tool reports problems as results, not as errors.

Extract any PDF up to 10 MB by URL. format='text' returns one clean plain-text string; format='json' returns a per-page text array plus document metadata. Parsed in-Worker, no upstream service. Paid: call without x_payment to receive this call's exact terms (amount, asset, network), sign them, then call again with x_payment. The free `pricing` tool lists every price at once.

Merge multiple PDF files into one, in the order given. Returns the merged PDF as base64.

Split a PDF into parts. Provide ranges like "1-3, 5, 8-10" (each comma group becomes one output PDF), or omit ranges to get every page as its own PDF. Returns base64 files.

Extract the given pages into a new PDF (original order). Returns base64.

Delete the given pages from a PDF. Returns the remaining document as base64.

Rotate pages clockwise by 90, 180, or 270 degrees. Rotates all pages unless a page list is given. Returns base64.

Stamp a diagonal text watermark (e.g. "CONFIDENTIAL", "DRAFT") across every page. Returns base64.

Return the authenticated PDF endpoint after status reaches ready. Use the same bearer token when downloading it.

Return the authenticated PDF endpoint after the Check is ready.

Docket entries for a case, ordered by itemNumber ascending. Each entry includes docket_id — call get_document_tool with that docket_id (no accept_charge) for an inline cost preview, then again with accept_charge: true to download the PDF. Pass simple_name (preferred, see list_monitored_cases_tool) or short_case_number / case_number (e.g. 26-42901; use court_state to disambiguate). Single-Case Agent: monitored cases only. limit max 50 (default 25); pass skip (or offset, or 1-based page) to walk past the first block. Response includes total, skip, limit, returned, and has_more. Optional live_update refreshes from court sources first. Subscribers only.

Preview the cost to download a court filing PDF. Identify the entry with docket_id (preferred) or simple_name + item_number (legacy connectors). Does not pull the document and does not charge. Same preview as get_document_tool when accept_charge/download is omitted. Confirm the cost with the user before download. Cost is $0 if already purchased, or if the PDF is already in our library and the plan has flagFreeLibrary. Free-page allowances (if any) come from plan limitDocuments and apply to billable pulls. Returns page_count, chargeable_pages, cost, plan_limit_documents, pages_used_this_period, free_pages_remaining, document_in_library, and already_owned.

Court filing PDF. Identify the entry with docket_id (preferred) OR simple_name + item_number (legacy cached connectors). Step 1: cost preview — omit accept_charge/download or set false. Step 2: after user confirms, call with accept_charge:true or download:true to purchase and download (signed URL). Returns docket_id in the preview so follow-up calls can use the hash. Pulls from PACER when needed.

Check the FileToPDF API key and return the plan, remaining conversion credits, and subscription status. Free — costs no credits. Use this to verify the connection works.

Convert a file fetched from a public URL into a PDF. Auto-detects the engine from the extension: Office docs (DOCX, XLSX, PPTX, ODT, RTF, TXT, CSV…), images (PNG, JPG, WebP…), HTML, Markdown, or an existing PDF (passthrough). Costs 1 credit on success. Returns the PDF as an embedded resource.

Render a raw HTML string to a PDF using Chromium, with optional CSS and layout options. Costs 1 credit on success. Returns the PDF as an embedded resource.

Render a raw Markdown string to a PDF, with optional CSS and layout options. A sensible default stylesheet is applied when no CSS is given. Costs 1 credit on success. Returns the PDF as an embedded resource.

List Kamy's public system PDF templates. No authentication required.

Fetch the JSON Schema (exact data shape) and a copy-pasteable sample payload for a Kamy system template by slug. Call this before render_pdf so you fill the right fields with the right types instead of guessing. No authentication required.

Create a new custom PDF template owned by this account: a name, a unique slug, a Handlebars/HTML body, and optional CSS and JSON Schema. The slug is what render_pdf then takes as `template`. Custom templates are a paid feature — on the Free plan this returns 403 FORBIDDEN before anything is created. The slug must be unique within the account and must not collide with a built-in system slug (invoice, receipt, quote, …); both cases come back 422, and the fix for an already-taken slug is update_template rather than a retry. A brand-new template has no published version, so it renders straight from what you supply here — publish_template only becomes necessary once you start cutting versions. Requires a Kamy API key with the `templates:write` scope; without a key, returns dashboard setup instructions.

Edit an existing custom template's DRAFT — name, html, css, schema, tags, visibility — addressed by UUID or slug. Only the fields you pass are changed. Crucially, this does not change what renders once the template has been published even once: while published_version is set, render_pdf serves that frozen snapshot and your edits stay invisible until publish_template moves the pointer, so an edit that appears to do nothing usually needs a publish. System templates, and templates belonging to another account, return 403. Requires a Kamy API key with the `templates:write` scope; without a key, returns dashboard setup instructions.

Render a PDF from a Kamy template and data, and wait for it. This is the default document tool: it blocks until the file exists and hands back { id, url, bytes, durationMs, templateId, createdAt } in one call, where url is a signed download link valid for one hour and id is the render id every later tool takes. Reach for render_async instead when waiting is not acceptable, and render_batch when several documents are wanted at once. Call get_template_schema first if you are unsure what fields the template expects. Counts one render against the monthly quota — get_account tells you what is left before this fails with 402. Requires a Kamy API key with the `render` scope; without a key, returns dashboard setup instructions.

Render an editable Word (.docx) document from a Kamy template and data. Takes the same { template, data } payload as render_pdf but produces a different container — reach for it when the recipient has to EDIT the document (legal redlines, Word-based intake, corporate templates) rather than receive a fixed artifact. Only five slugs have a Word implementation — invoice, receipt, quote, contract, agreement — and any other template is rejected with a validation error; use render_pdf for those. Returns { id, url, bytes, durationMs, format: 'docx' }, where url is a signed download link valid for one hour and id is a normal render id. Counts one render against the monthly quota. Requires a Kamy API key with the `render` scope; without a key, returns dashboard setup instructions.

Generate a draft of the document from the user's answers. Returns a free watermarked PDF preview and the exact price. Validates all answers against the encoded national rules first — if validation fails, you get structured errors to relay to the user. Also returns what is NOT covered by the automated checks: always relay the not_covered items to the user.

Purchase the final document for a given draft. Returns a secure payment link to give to the user. After payment, the final PDF is emailed to the user within minutes. E-signature is not yet available.

Convert a public webpage to a PDF and return a signed download URL (valid for 7 days). Deliberately narrow: one URL in, one PDF out. Requires a urltopdf.org Developer API key.

把多个 PDF 按顺序合并成一个文件（1 credit/次）。纯本地操作，失败自动退款。

抽取指定页另存为新 PDF（1 credit/次）。失败自动退款。

压缩 PDF 体积（清理冗余 + 重压缩流/图片/字体，1 credit/次）。失败自动退款。

给每页加居中、45° 半透明文字水印（1 credit/次）。失败自动退款。

给 PDF 设打开密码（AES-256，1 credit/次）。失败自动退款。

用已知密码解密 PDF、输出无密码版本（1 credit/次）。密码错误会报错并退款。

Upload a file (.txt, .md, .pdf, .docx, .rtf -- no legacy .doc) and get the extracted text back scrubbed of PII, plus the detected format and extracted word count. $0.01 USDC per call regardless of length -- this route always requires payment. No OCR -- PDFs need embedded/selectable text. Zero storage: the file is never written to disk, on this service or the extractor. Free tier: use scrub_file_free / POST /scrub-file-free for files at or under 1000 extracted words instead.

Free version of scrub_file for files whose extracted text is at or under 1000 words. No payment, no API key. Upload a file (.txt, .md, .pdf, .docx, .rtf -- no legacy .doc), get the extracted text back scrubbed of PII plus detected format and word count. No OCR. Zero storage: never written to disk, on this service or the extractor. Over the limit returns 413 naming the limit and your actual extracted word count -- this route never takes payment. For larger files, use scrub_file ($0.01).